import torch
import tqdm
from sklearn.metrics import roc_auc_score
from torch.utils.data import DataLoader

from torchfm.dataset.avazu import AvazuDataset
from torchfm.dataset.criteo import CriteoDataset
from torchfm.dataset.movielens import MovieLens1MDataset, MovieLens20MDataset
from torchfm.model.afi import AutomaticFeatureInteractionModel
from torchfm.model.afm import AttentionalFactorizationMachineModel
from torchfm.model.dcn import DeepCrossNetworkModel
from torchfm.model.dfm import DeepFactorizationMachineModel
from torchfm.model.ffm import FieldAwareFactorizationMachineModel
from torchfm.model.fm import FactorizationMachineModel
from torchfm.model.fnfm import FieldAwareNeuralFactorizationMachineModel
from torchfm.model.fnn import FactorizationSupportedNeuralNetworkModel
from torchfm.model.hofm import HighOrderFactorizationMachineModel
from torchfm.model.lr import LogisticRegressionModel
from torchfm.model.ncf import NeuralCollaborativeFiltering
from torchfm.model.nfm import NeuralFactorizationMachineModel
from torchfm.model.pnn import ProductNeuralNetworkModel
from torchfm.model.wd import WideAndDeepModel
from torchfm.model.xdfm import ExtremeDeepFactorizationMachineModel
from torchfm.model.afn import AdaptiveFactorizationNetwork


def get_dataset(name, path):
    if name == 'movielens1M':
        return MovieLens1MDataset(path) # 返回一个数据类，这个要好好学
    elif name == 'movielens20M':
        return MovieLens20MDataset(path)
    elif name == 'criteo':
        return CriteoDataset(path)
    elif name == 'avazu':
        return AvazuDataset(path)
    else:
        raise ValueError('unknown dataset name: ' + name)


def get_model(name, dataset):
    """
    Hyperparameters are empirically determined, not opitmized.
    """
    field_dims = dataset.field_dims
    if name == 'lr':
        return LogisticRegressionModel(field_dims)
    elif name == 'fm':
        return FactorizationMachineModel(field_dims, embed_dim=16)
    elif name == 'hofm':
        return HighOrderFactorizationMachineModel(field_dims, order=3, embed_dim=16)
    elif name == 'ffm':
        return FieldAwareFactorizationMachineModel(field_dims, embed_dim=4)
    elif name == 'fnn':
        return FactorizationSupportedNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)
    elif name == 'wd':
        return WideAndDeepModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)
    elif name == 'ipnn':
        return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='inner', dropout=0.2)
    elif name == 'opnn':
        return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='outer', dropout=0.2)
    elif name == 'dcn':
        return DeepCrossNetworkModel(field_dims, embed_dim=16, num_layers=3, mlp_dims=(16, 16), dropout=0.2)
    elif name == 'nfm':
        return NeuralFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(64,), dropouts=(0.2, 0.2))
    elif name == 'ncf':
        # only supports MovieLens dataset because for other datasets user/item colums are indistinguishable
        assert isinstance(dataset, MovieLens20MDataset) or isinstance(dataset, MovieLens1MDataset)
        return NeuralCollaborativeFiltering(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2,
                                            user_field_idx=dataset.user_field_idx,
                                            item_field_idx=dataset.item_field_idx)
    elif name == 'fnfm':
        return FieldAwareNeuralFactorizationMachineModel(field_dims, embed_dim=4, mlp_dims=(64,), dropouts=(0.2, 0.2))
    elif name == 'dfm':
        return DeepFactorizationMachineModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)
    elif name == 'xdfm':
        return ExtremeDeepFactorizationMachineModel(
            field_dims, embed_dim=16, cross_layer_sizes=(16, 16), split_half=False, mlp_dims=(16, 16), dropout=0.2)
    elif name == 'afm':
        return AttentionalFactorizationMachineModel(field_dims, embed_dim=16, attn_size=16, dropouts=(0.2, 0.2))
    elif name == 'afi':
        return AutomaticFeatureInteractionModel(
             field_dims, embed_dim=16, atten_embed_dim=64, num_heads=2, num_layers=3, mlp_dims=(400, 400), dropouts=(0, 0, 0))
    elif name == 'afn':
        print("Model:AFN")
        return AdaptiveFactorizationNetwork(
            field_dims, embed_dim=16, LNN_dim=1500, mlp_dims=(400, 400, 400), dropouts=(0, 0, 0))
    else:
        raise ValueError('unknown model name: ' + name)


class EarlyStopper(object):

    def __init__(self, num_trials, save_path):
        self.num_trials = num_trials
        self.trial_counter = 0
        self.best_accuracy = 0
        self.save_path = save_path

    def is_continuable(self, model, accuracy):
        if accuracy > self.best_accuracy:
            self.best_accuracy = accuracy
            self.trial_counter = 0
            # 第一次运行应该需要建立一个cnkpt（默认）的文件夹，他这个代码不会自动建文件夹。否则会报错
            torch.save(model, self.save_path)
            return True
        elif self.trial_counter + 1 < self.num_trials:
            self.trial_counter += 1
            return True
        else:
            return False


def train(model, optimizer, data_loader, criterion, device, log_interval=100):
    model.train()
    total_loss = 0
    # tqdm 是进度条工具
    tk0 = tqdm.tqdm(data_loader, smoothing=0, mininterval=1.0)
    for i, (fields, target) in enumerate(tk0):
        fields, target = fields.to(device), target.to(device)
        # fields是IntTensor,需要转换为Long,一个bug，也可能是版本的问题？
        fields = fields.long()
        y = model(fields)
        loss = criterion(y, target.float())
        model.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
        if (i + 1) % log_interval == 0:
            tk0.set_postfix(loss=total_loss / log_interval)
            total_loss = 0


def test(model, data_loader, device):
    model.eval()
    targets, predicts = list(), list()
    with torch.no_grad():
        for fields, target in tqdm.tqdm(data_loader, smoothing=0, mininterval=1.0):
            fields, target = fields.to(device), target.to(device)
            # fields是IntTensor,需要转换为Long,一个bug，也可能是版本的问题？
            fields = fields.long()
            y = model(fields)
            targets.extend(target.tolist())
            predicts.extend(y.tolist())
    return roc_auc_score(targets, predicts)


def main(dataset_name,
         dataset_path,
         model_name,
         epoch,
         learning_rate,
         batch_size,
         weight_decay,
         device,
         save_dir):
    device = torch.device(device)
    dataset = get_dataset(dataset_name, dataset_path)
    train_length = int(len(dataset) * 0.8)
    valid_length = int(len(dataset) * 0.1)
    test_length = len(dataset) - train_length - valid_length  # 划分数据集的策略，最后通过减法确保数据整数。
    train_dataset, valid_dataset, test_dataset = torch.utils.data.random_split(
        dataset, (train_length, valid_length, test_length)) # 这个地方是如何保证每次划分的是相同的，还是每次都不同？
    train_data_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=8)
    valid_data_loader = DataLoader(valid_dataset, batch_size=batch_size, num_workers=8)
    test_data_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=8)
    model = get_model(model_name, dataset).to(device)
    criterion = torch.nn.BCELoss()
    optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate, weight_decay=weight_decay)
    early_stopper = EarlyStopper(num_trials=2, save_path=f'{save_dir}/{model_name}.pt')     # 这种变量构造字符串的方式要会用
    for epoch_i in range(epoch):
        train(model, optimizer, train_data_loader, criterion, device)
        auc = test(model, valid_data_loader, device)
        print('epoch:', epoch_i, 'validation: auc:', auc)
        if not early_stopper.is_continuable(model, auc):
            print(f'validation: best auc: {early_stopper.best_accuracy}')
            break
    auc = test(model, test_data_loader, device)
    print(f'test auc: {auc}')


if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset_name', default='movielens1M')
    parser.add_argument('--dataset_path', default='ml-1m/ratings.dat', help='criteo/train.txt, avazu/train, or ml-1m/ratings.dat')
    parser.add_argument('--model_name', default='lr')
    parser.add_argument('--epoch', type=int, default=1)
    parser.add_argument('--learning_rate', type=float, default=0.001)
    parser.add_argument('--batch_size', type=int, default=1024)
    parser.add_argument('--weight_decay', type=float, default=1e-6)
    parser.add_argument('--device', default='cpu', help='cuda:0, cpu')
    parser.add_argument('--save_dir', default='chkpt')
    args = parser.parse_args()
    main(args.dataset_name,
         args.dataset_path,
         args.model_name,
         args.epoch,
         args.learning_rate,
         args.batch_size,
         args.weight_decay,
         args.device,
         args.save_dir)
